Empirical Evaluation of Machine Learning Models for Fuel Consumption, Driver Identification, and Behavior Prediction

Drivers can be identified through patterns in their routine driving behaviours, as observed by analysing the timing and sequence of various manoeuvres. In contemporary mobility contexts, comprehending and accurately predicting drivers' behaviours are crucial for informing efficient transportation planning, enhancing traffic safety, reducing emissions, and improving driving efficiency. An increasing number of researchers have explored a variety of machine learning (ML) models to identify, classify, and predict drivers' behaviours. However, the reliability of these results is often undermined by the complexities associated with the data characteristics, contexts, and the authors' expertise. Additionally, there is a lack of comprehensive investigation into the effect of driving behaviour on vehicles' performance, driver identity, and driving activities. This research aims to compare various ML methods to establish a conclusive and generalisable empirical benchmark. The experiments were divided into three phases: estimation of fuel consumption, driver identification, and driver actions' prediction from drivers' behaviour during motion. The experiments evaluate prediction accuracy, performance, and computational cost using a different range of temporal and nontemporal ML models and eight datasets from diverse sources, which resulted in 9 tables of outputs. The results have been gauged and scored precisely, and then high-rated and ineffective algorithms were pinpointed for each task. This study is the most in-depth investigation, providing an exhaustive comparison of different ML models for predicting three main criteria of driving behaviour, marking it as the most detailed investigation in this field.